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We combine this process with a lower-level reactive control accounts for stochastic target motion. By making this controller aware of a viewpoint cost function, the behavior of the tracking agents can be both more performant and easier to deploy on real robots.","abstract_html":"We seek to combine high level planning with low level reactive control to solve a variety of viewpoint-constrained target following tasks. In the scenarios we consider, a team of tracking agents is desired to gain some sort of visual information about one or more target agents. A high level planning algorithm accounts for coarse, global decisions, such as “Which targets should each tracker be responsible for?”, or “When should a tracker visit each target?” This level of planning is combinatorial in nature and requires coordination between the tracking agents. We combine this process with a lower-level reactive control accounts for stochastic target motion. By making this controller aware of a viewpoint cost function, the behavior of the tracking agents can be both more performant and easier to deploy on real robots.","abstract_has_math":false,"creators":["Ray, Aaron Castagna"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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In the scenarios we consider, a team of tracking agents is desired to gain some sort of visual information about one or more target agents. A high level planning algorithm accounts for coarse, global decisions, such as “Which targets should each tracker be responsible for?”, or “When should a tracker visit each target?” This level of planning is combinatorial in nature and requires coordination between the tracking agents. We combine this process with a lower-level reactive control accounts for stochastic target motion. By making this controller aware of a viewpoint cost function, the behavior of the tracking agents can be both more performant and easier to deploy on real robots."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Viewpoint-Aware Task Planning and Model Predictive Control for Applications in Videography and Multi-Target Tracking"]}]}],"canonical_facts":{"dc:contributor.advisor":["Rus, Daniela"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Ray, Aaron Castagna"],"dc:date.accessioned":["2022-02-07T15:11:40Z"],"dc:date.available":["2022-02-07T15:11:40Z"],"dc:date.issued":["2021-09"],"dc:description.abstract":["We seek to combine high level planning with low level reactive control to solve a variety of viewpoint-constrained target following tasks. In the scenarios we consider, a team of tracking agents is desired to gain some sort of visual information about one or more target agents. A high level planning algorithm accounts for coarse, global decisions, such as “Which targets should each tracker be responsible for?”, or “When should a tracker visit each target?” This level of planning is combinatorial in nature and requires coordination between the tracking agents. We combine this process with a lower-level reactive control accounts for stochastic target motion. By making this controller aware of a viewpoint cost function, the behavior of the tracking agents can be both more performant and easier to deploy on real robots."],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/139901"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Viewpoint-Aware Task Planning and Model Predictive Control for Applications in Videography and Multi-Target Tracking"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Science in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:21:05Z"}